HCPLive

Reliable Emotion Words Identified to Assess Patient Experience

THURSDAY, Jan. 16, 2014 (HealthDay News) -- A reliable set of emotion words have been identified that can serve as a tool for experience-based design questionnaires in health care, according to a study published in the December issue of Healthcare.

Lauren R. Russ, from the Virginia Mason Medical Center in Seattle, and colleagues surveyed 407 patients, family members, and health care workers in 2011. Participants rated each of 67 potential words as positive, neutral, or negative based on their emotional perception of the word. Words with 80 percent simple agreement in classification were retained in the final emotion word set.

The researchers found that, after adjusting for chance, overall agreement was moderate (κ = 0.55). For positive and negative emotions, the agreement was much higher (κ = 0.69 and 0.68, respectively). Agreement for neutral words was low (κ = 0.11). For the final survey-informed emotion word set there were 20 positive words, one neutral word, and 14 negative words retained.

"We identified a reliable set of emotion words for experience questionnaires to serve as the foundation for patient-centered, experience-based redesign of health care," the authors write.

Full Text


Copyright © 2014 HealthDay. All rights reserved.



Most Popular

Recommended Reading

A recent study looked into the neural functions affected by ibuprofen and found some connections that may soon lead to a much greater understanding of the greatest pain mitigator of them all: the brain.
The US Food and Drug Administration (FDA) on Thursday approved Orkambi (lumacaftor/ivacaftor/Vertex) to treat cystic fibrosis patients age 12 and older with two copies of the F508del mutation in the CFTR gene.
Researchers from the University of California-Santa Barbara recently announced they have created an implantable “artificial pancreas” that could potentially eliminate the need for insulin injections and pumps for treating type 1 diabetes.
Michigan State University (MSU) researchers have created the first map to predict rotavirus (RV) prevalence on a global scale.
$vAR$